aceTransfer-Aided Deep Learning for Life Cycle Assessment Prediction of Precursor Molecules
To date the environmental impacts of production of most chemicals are unknown, hindering sustainable process evaluation and design. Life cycle assessment (LCA) is the most comprehensive method for estimating these environmental impacts but existing LCA databases cover a small fraction of the industrial chemicals. Environmental impact prediction models promise to bridge this gap; several models were published, with the apparent limitations of limited accuracy and applicability. The very small number of available…
Supervised pretraining on molecular price data raised the accuracy of deep learning models that predict environmental impacts of chemicals, easing the shortage of life cycle assessment data.
The approach only partly addresses the limits of accuracy and applicability, since accuracy improved in only two of three impact categories and the carbon footprint R 2 of 0.62 is modest.
Evidence
- Peer-reviewedChemSusChem2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1500, v1: “Transfer-Aided Deep Learning for Life Cycle Assessment Prediction of Precursor Molecules.” Truvace, 2026-10-15. /record/TRV-2026-1500 (accessed at citation time). sha256 128e3b55affd88f7…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1500 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.